Bounding probabilities of causation through the causal marginal problem

• URL: http://arxiv.org/abs/2304.02023v1
• Date: Tue, 4 Apr 2023 12:16:38 GMT
• Title: Bounding probabilities of causation through the causal marginal problem
• Authors: Numair Sani, Atalanti A. Mastakouri, Dominik Janzing
• Abstract summary: Probabilities of causation play a fundamental role in decision making in law, health care and public policy. In many clinical trials and public policy evaluation cases, there exist independent datasets that examine the effect of a different treatment each on the same outcome variable. Here, we outline how to significantly tighten existing bounds on the probabilities of causation, by imposing counterfactual consistency between SCMs constructed from such independent datasets.
• Score: 12.542533707005092
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Probabilities of Causation play a fundamental role in decision making in law, health care and public policy. Nevertheless, their point identification is challenging, requiring strong assumptions such as monotonicity. In the absence of such assumptions, existing work requires multiple observations of datasets that contain the same treatment and outcome variables, in order to establish bounds on these probabilities. However, in many clinical trials and public policy evaluation cases, there exist independent datasets that examine the effect of a different treatment each on the same outcome variable. Here, we outline how to significantly tighten existing bounds on the probabilities of causation, by imposing counterfactual consistency between SCMs constructed from such independent datasets ('causal marginal problem'). Next, we describe a new information theoretic approach on falsification of counterfactual probabilities, using conditional mutual information to quantify counterfactual influence. The latter generalises to arbitrary discrete variables and number of treatments, and renders the causal marginal problem more interpretable. Since the question of 'tight enough' is left to the user, we provide an additional method of inference when the bounds are unsatisfactory: A maximum entropy based method that defines a metric for the space of plausible SCMs and proposes the entropy maximising SCM for inferring counterfactuals in the absence of more information.

Related papers

• On the Identification of Temporally Causal Representation with Instantaneous Dependence [50.14432597910128]
  Temporally causal representation learning aims to identify the latent causal process from time series observations. Most methods require the assumption that the latent causal processes do not have instantaneous relations. We propose an textbfIDentification framework for instantanetextbfOus textbfLatent dynamics.
  arXiv  Detail & Related papers  (2024-05-24T08:08:05Z)
• Tightening Bounds on Probabilities of Causation By Merging Datasets [0.0]
  Probabilities of Causation (PoC) play a fundamental role in decision-making in law, health care and public policy. Existing work to further tighten these bounds by leveraging extra information either provides numerical bounds, symbolic bounds for fixed dimensionality, or requires access to multiple datasets that contain the same treatment and outcome variables. Here, we provide symbolic bounds on the PoC for a challenging scenario.
  arXiv  Detail & Related papers  (2023-10-12T15:19:15Z)
• Identifiability Guarantees for Causal Disentanglement from Soft Interventions [26.435199501882806]
  Causal disentanglement aims to uncover a representation of data using latent variables that are interrelated through a causal model. In this paper, we focus on the scenario where unpaired observational and interventional data are available, with each intervention changing the mechanism of a latent variable. When the causal variables are fully observed, statistically consistent algorithms have been developed to identify the causal model under faithfulness assumptions.
  arXiv  Detail & Related papers  (2023-07-12T15:39:39Z)
• Nonparametric Identifiability of Causal Representations from Unknown Interventions [63.1354734978244]
  We study causal representation learning, the task of inferring latent causal variables and their causal relations from mixtures of the variables. Our goal is to identify both the ground truth latents and their causal graph up to a set of ambiguities which we show to be irresolvable from interventional data.
  arXiv  Detail & Related papers  (2023-06-01T10:51:58Z)
• Causal Discovery via Conditional Independence Testing with Proxy Variables [35.3493980628004]
  The presence of unobserved variables, such as the latent confounder, can introduce bias in conditional independence testing. We propose a novel hypothesis-testing procedure that can effectively examine the existence of the causal relationship over continuous variables.
  arXiv  Detail & Related papers  (2023-05-09T09:08:39Z)
• Causal Inference Through the Structural Causal Marginal Problem [17.91174054672512]
  We introduce an approach to counterfactual inference based on merging information from multiple datasets. We formalise this approach for categorical SCMs using the response function formulation and show that it reduces the space of allowed marginal and joint SCMs.
  arXiv  Detail & Related papers  (2022-02-02T21:45:10Z)
• Obtaining Causal Information by Merging Datasets with MAXENT [12.64433334351049]
  We discuss how causal knowledge can be obtained without having observed all variables jointly. We derive bounds on the interventional distribution and the average causal effect of a treatment on a target variable in the presence of confounders.
  arXiv  Detail & Related papers  (2021-07-15T23:16:36Z)
• Variational Causal Networks: Approximate Bayesian Inference over Causal Structures [132.74509389517203]
  We introduce a parametric variational family modelled by an autoregressive distribution over the space of discrete DAGs. In experiments, we demonstrate that the proposed variational posterior is able to provide a good approximation of the true posterior.
  arXiv  Detail & Related papers  (2021-06-14T17:52:49Z)
• BayesIMP: Uncertainty Quantification for Causal Data Fusion [52.184885680729224]
  We study the causal data fusion problem, where datasets pertaining to multiple causal graphs are combined to estimate the average treatment effect of a target variable. We introduce a framework which combines ideas from probabilistic integration and kernel mean embeddings to represent interventional distributions in the reproducing kernel Hilbert space.
  arXiv  Detail & Related papers  (2021-06-07T10:14:18Z)
• Deconfounded Score Method: Scoring DAGs with Dense Unobserved Confounding [101.35070661471124]
  We show that unobserved confounding leaves a characteristic footprint in the observed data distribution that allows for disentangling spurious and causal effects. We propose an adjusted score-based causal discovery algorithm that may be implemented with general-purpose solvers and scales to high-dimensional problems.
  arXiv  Detail & Related papers  (2021-03-28T11:07:59Z)
• Disentangling Observed Causal Effects from Latent Confounders using Method of Moments [67.27068846108047]
  We provide guarantees on identifiability and learnability under mild assumptions. We develop efficient algorithms based on coupled tensor decomposition with linear constraints to obtain scalable and guaranteed solutions.
  arXiv  Detail & Related papers  (2021-01-17T07:48:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.